جستجوی مقالات مرتبط با کلیدواژه "تحلیل فراوانی" در نشریات گروه "آب و خاک"

Frequency analysis of daily rainfall or return period of rainfall and flooding events is very important considering the behavioral complexity in water resources management; because ignoring it can lead to urban destructive floods. In the present research, three distribution functions of Pearson, Beta, and Gamma were compared to investigate and select the most appropriate distribution function for the precipitation data acquired from meteorology stations and CHIRPS satellite in seven stations in the watershed of Bustan Dam. Statistical analyses showed that satellite data were ineffective to estimate daily precipitation due to high errors in RMSE, MAD, and NASH. Meteorological data were used to spot the best distribution. Google Earth Engine and Python programming language were used. Then, the selected distribution function was used to determine the maximum daily rainfall, frequency probability, and return period of 2, 10, 50, 100, and 200 years. The results of the goodness of fit test, Error Sum of Squares, Bayesian Information Criterion, Akaike Information Criteria well as Kullback-Leibler Divergence showed that in five stations of Kalaleh, Qarnaq, Golestan National Park, Golestan Dam, and Glidagh, the Pearson function is the most suitable distribution function. Also, in the other two stations (Gonbad and Tamar), the Beta function was recognized as a suitable function. However, Gamma distribution in the study area is not efficient. So, it can be concluded that heavy and irregular rainfall can be effective in choosing the best distribution function at each station. Therefore, it is recommended to consider the maximum possible rainfall and as a result of the possible occurrence of floods with principled and accurate management to prevent human and financial losses in susceptible areas, especially in the study area.

One of the important relationships which are used in the estimation of river discharges and floods is Intensity-Duration-Frequency (IDF). The accuracy of this relation is dependent on the accuracy of its parameters which need to be found based on short-duration rainfall depths (such as 15, 30, 60 minutes, and so on) for a long term (i. e. 30 consecutive years). Unfortunately, only 24-hour rainfall depths are available in many rainfall stations in Iran. Various empirical relations are available to convert 24-hour rainfall depth to sub-daily. One of these methods is IMD and its accuracy in some regions is low. In this research, the IMD method was transformed into a single-parameter equation and then, this parameter is evaluated for some rainfall stations in Iran. To do this, maximum 24, 12, 6, and 3-hour rainfall depths were extracted and their frequencies were calculated using Weibull and Gumbel methods. Regional coefficients in the modified IMD method were estimated using a linear regression method. Although the power of the IMD method is 0.33, results showed that this parameter for the rainfall stations ranged from 0.28 to 0.35. To make more comparison, the IDF relation of Kordan’s watershed was calculated using the short-duration rainfall depth which was estimated using the modified IMD, and then, this IDF was compared to observed data and Ghahraman’s relation which is commonly used in Iran. The comparison showed that the modified IMD relation could estimate the short-duration rainfall data better than Ghahraman’s relation. After calibration of the modified IMD relation for various regions in Iran, the sub-daily rainfall depth can be obtained with high accuracy.

Rain water harvesting methods is the include of management methods in order to encounter and compatibility with the conditions of the lack of water. Base of these methods, is allocating surfaces of the earth in order to collecting, storing and reusing rain water for designing goals at the times needed. The necessity of the implementation of the plan is that Were seeing that in any relatively intense rainfall occurs, The Eclipse occurs on roads and utilities and the damage to the House, that is required to a big city like Tabriz to have a macro system to reduce any damages caused by the floods and also be able to take maximum advantage of this current of water. In present research, In order to increase accuracy in using the methods of estimating runoff, facilitate the access and reduce the cost of transferring, the study area was divided to 4 sub drainage basin. Runoff derived of the four zones was computed with the SCS method. The volume of the reservoirs were designed with the use of the method of runoff frequency analysis for different seasons and with different return periods. Comparing green landscape monthly water requirement with rain water of Tabriz, clears that reservoirs are able to provide about 48 percent of the need for green landscape and the rest of the need, can be provided through the underground water and the incoming water supply to the city.

Climate change with population growth makes the water resources an important issue in Iran. Drought is an abnormal dry period which lasted for a long time. Frequency analysis of drought and prediction of drought deficit volume (drought severity) for a determined period and return period are the most important issues in drought studies. Frequency analysis is a technique of fitting a probability distribution to a series of observations for providing the probabilities of future occurrences. Also, this technique is the statistical analysis of series that have a suitable probability distribution. Inappropriate temporal and spatial distribution of rainfall exacerbate water resources management problems in addition to rainfall deficit. We can consider drought as an inappropriate temporal distribution of rainfall, So we feel the necessity of drought studies in different aspects. There are various studies for meteorological, hydrological and agricultural droughts but it seems we have not any specific research about relationship between intensity and duration frequency of both meteorological and hydrological droughts. Therefore, the purpose of this study is to determine a consolidated statistical indicator based on meteorological and hydrological droughts in regional scale.
This drought study is described in Soufi-Chai basin with 265 km2 in East Azerbaijan which is located in northwestern Iran. The monthly flow and precipitation data of Soufi-Chai's stations was used. The observed data consists of 40 years (480 months) period between 1970 and 2011. Then the monthly precipitation data have been classified by using Thiessen Polygon for meteorological droughts and the monthly flow data of Tazeh-Kand station in outlet was considered for hydrological droughts. In order to extract the amount of drought periods for both flow and precipitation, monthly normal regime method was applied. Monthly normal series show a long-term average for each month of a year that presents monthly normal regime for a normal year. Anomaly series for both flow and precipitation data were made by subtracting flow and precipitation data from monthly normal series. In fact, we considered monthly normal series as base level and amounts under this level (negative amounts) introduced as dry periods. Then these droughts was converted to the volume deficits that showed by duration one month, two months, …and up to twelve months. In order to fit suitable distribution and extract IDF (Intensity- Duration- Frequency) curves, frequency analysis upon volume deficit was done by using statistical distribution methods. In this study, SMADA software was used for frequency analysis. In this work, root mean square error (RMSE) were used to assess different distributions. For various return periods (2, 3, 5, 10, 25, 50, 100 and 200) the precipitation and flow drought volume deficits were calculated by using Normal and Pearson type III distributions. We eliminated temporal factor and only based on various return periods for extracting a model which shows a relationship between both meteorological and hydrological droughts. At first, a statistical model was obtained separately for each drought and then a new index was created by eliminating temporal factor as below:This index is an exponentially equation which only related to return period. For investigating performance of this index, correlation coefficient (R) was used. R values had high amounts that shows a desirable result for this equation. This index is so important in reservoirs and dam management.
As a result of this study, we considered average amount as a management line, so that, drought will be the precipitation or flow under the average. The meteorological and hydrological drought graphs show that in most years, hydrological drought has not occurred in those months which have meteorological drought, because of some reasons such as delay in snow melting. So hydrological droughts have occurred in few months later. Therefore, it takes time to affect different elements of hydrologic system like river, reservoirs and underground waters by precipitation deficit. A comparison between IDF curves showed that hydrological droughts take place with more intensity rather than meteorological droughts. Then forecasting hydrological droughts by meteorological droughts done by frequency analyzing. The results showed that Normal distribution has the least root mean square error (RMSE) for precipitation volume deficits in duration from one month to nine, respectively: (1.07, 1.950, 2.043, 1.917, 1.924, 1.981, 2.641, 3.515, 5.489) and Pearson type ш for flow volume deficits (1.179, 2, 2.663, 3.101, 3.508, 3.812, 4.321, 4.828, 5.029). By using these statistical distributions, severity- duration- frequency curves were extracted and an appropriate mathematical equation based on correlation coefficient and also the least error has been selected. Then the relationship between meteorological and hydrological droughts was earned exponentially independent of time and only based on frequency. The results of this study are very noteworthy for droughts management.

Investigating lowflow characteristics is of vital importance for optimal water resources management and sustainable use of water resources especially in arid and semi arid regions. In this study 26 hydromerty stations in the Sefidroud basin were selected and 7-day annual minimum series were computed as low flow index. Physiographic characteristics of the basin were also gathered in GIS software. Perason type 3 (PIII) and Generalized logistic distributions (GLOG) were identified as the best regional distributions for the west and east homogeneous regions respectively based on the goodness-of-fit test. Low flow regional regression (LFRR) was then developed for each homogeneous regions. According to the established models, drainage area, land use characteristics, average basin slope and basin circularity were identified as important factors in low flows features.Investigating lowflow characteristics is of vital importance for optimal water resources management and sustainable use of water resources especially in arid and semi arid regions. In this study 26 hydromerty stations in the Sefidroud basin were selected and 7-day annual minimum series were computed as low flow index. Physiographic characteristics of the basin were also gathered in GIS software. Perason type 3 (PIII) and Generalized logistic distributions (GLOG) were identified as the best regional distributions for the west and east homogeneous regions respectively based on the goodness-of-fit test. Low flow regional regression (LFRR) was then developed for each homogeneous regions. According to the established models, drainage area, land use characteristics, average basin slope and basin circularity were identified as important factors in low flows features.Investigating lowflow characteristics is of vital importance for optimal water resources management and sustainable use of water resources especially in arid and semi arid regions. In this study 26 hydromerty stations in the Sefidroud basin were selected and 7-day annual minimum series were computed as low flow index. Physiographic characteristics of the basin were also gathered in GIS software. Perason type 3 (PIII) and Generalized logistic distributions (GLOG) were identified as the best regional distributions for the west and east homogeneous regions respectively based on the goodness-of-fit test. Low flow regional regression (LFRR) was then developed for each homogeneous regions. According to the established models, drainage area, land use characteristics, average basin slope and basin circularity were identified as important factors in low flows features.Investigating lowflow characteristics is of vital importance for optimal water resources management and sustainable use of water resources especially in arid and semi arid regions. In this study 26 hydromerty stations in the Sefidroud basin were selected and 7-day annual minimum series were computed as low flow index. Physiographic characteristics of the basin were also gathered in GIS software. Perason type 3 (PIII) and Generalized logistic distributions (GLOG) were identified as the best regional distributions for the west and east homogeneous regions respectively based on the goodness-of-fit test. Low flow regional regression (LFRR) was then developed for each homogeneous regions. According to the established models, drainage area, land use characteristics, average basin slope and basin circularity were identified as important factors in low flows features.Investigating lowflow characteristics is of vital importance for optimal water resources management and sustainable use of water resources especially in arid and semi arid regions. In this study 26 hydromerty stations in the Sefidroud basin were selected and 7-day annual minimum series were computed as low flow index. Physiographic characteristics of the basin were also gathered in GIS software. Perason type 3 (PIII) and Generalized logistic distributions (GLOG) were identified as the best regional distributions for the west and east homogeneous regions respectively based on the goodness-of-fit test. Low flow regional regression (LFRR) was then developed for each homogeneous regions. According to the established models, drainage area, land use characteristics, average basin slope and basin circularity were identified as important factors in low flows features.Investigating lowflow characteristics is of vital importance for optimal water resources management and sustainable use of water resources especially in arid and semi arid regions. In this study 26 hydromerty stations in the Sefidroud basin were selected and 7-day annual minimum series were computed as low flow index. Physiographic characteristics of the basin were also gathered in GIS software. Perason type 3 (PIII) and Generalized logistic distributions (GLOG) were identified as the best regional distributions for the west and east homogeneous regions respectively based on the goodness-of-fit test. Low flow regional regression (LFRR) was then developed for each homogeneous regions. According to the established models, drainage area, land use characteristics, average basin slope and basin circularity were identified as important factors in low flows features.Investigating lowflow characteristics is of vital importance for optimal water resources management and sustainable use of water resources especially in arid and semi arid regions. In this study 26 hydromerty stations in the Sefidroud basin were selected and 7-day annual minimum series were computed as low flow index. Physiographic characteristics of the basin were also gathered in GIS software. Perason type 3 (PIII) and Generalized logistic distributions (GLOG) were identified as the best regional distributions for the west and east homogeneous regions respectively based on the goodness-of-fit test. Low flow regional regression (LFRR) was then developed for each homogeneous regions. According to the established models, drainage area, land use characteristics, average basin slope and basin circularity were identified as important factors in low flows features.

Evaluation of frequency distributions for rainfall erosivity factor analysis has been less considered. Thus, the present study aimed to identify and evaluate different frequency distributions fitted to the amount of rainfall erosivity factor in monthly, seasonal and annual scales in Iran. For this attempt, entire available rainfall events reported for 20 years in 70 stations were analyzed based on the Wischmeier and Smith equation. The different frequency distributions were then fitted to data in each of the time scale and station using EasyFit software with the help of chi-square test. According to results maximum Gumble distribution was selected as the best fitted distribution in most of months and seasons and also, 3-parameter lognormal, gamma and 2-parameter lognormal frequency distributions stood in the first rank among fitted frequency distributions. Also, stations with more stable rainfall regime and influenced by similar rainfall regimes had less variability in the type of fitted distributions in different time scales.

Due to the effects of climate change on water resources and hydrology, Changes in low flow as an important part of the water cycle, is of interest to researchers, water managers and users in various fields. Changes in characteristics of low flows affected by climate change may have important effects on various aspects of socioeconomic , environmental, water resources and governmental planning. There are several indices to assess the low flows. The used low flow indices in this research for assessing climate change impacts, is include the extracted indices from flow duration curve (Q70, Q90 and Q95), due to the importance of these indices in understanding and assessing the status of river flow in dry seasons that was investigated in Tang Panj Sezar basin in the west of Iran.
In this paper, the Tang Panj Sezar basin with an area of 9410 km2 was divided into 6 smaller sub catchments and the changes of low flow indices were studied in each of the sub catchments. In order to consider the effects of climate change on low flow, scenarios of temperature and precipitation using 10 atmospheric general circulation models (to investigate the uncertainty of GCMs) for both the baseline (1971-2000) and future (2011-2040) under A2 emission scenario was prepared. These scenarios, due to large spatial scale need to downscaling. Therefore, LARS-WG stochastic weather generator model was used. In order to consider the effects of climate change on low flows in the future, a hydrologic model is required to simulate daily flow for 2011-2040. The IHACRES rainfall-runoff model was used for this purpose . After simulation of daily flow using IHACRES, with two time series of daily flow for the observation and future period in each of the sub catchment, the low flow indices were compared.
Results According to results, across the whole year, the monthly temperature in the future period has increased while rainfall scenarios show different variations for different months, also within a month for different GCMs. Based on the results of low flow indices, in most cases, the three indices of Q70, Q90, and Q95 will show incremental changes in the future compared to the past. Also, the domain simulation by 10 GCMs for all three indices is maximum in Tang Panj Sezar and less for other sub catchments, which is related to better performance of IHACRES model in smaller sub catchments. In order to investigate the uncertainty of type changes in different indices in every sub catchment, changes in any of the indices were considered based on the median of GCMs. To achieve the correct type of changes in low flow indices, the amount of error in a simulation of the indices of IHACRES rainfall-runoff model should also be taken into consideration. Therefore, considering the error, the three indices Q70, Q90 and Q95 in all sub catchments (except for Tang Panj Sezar) will have the relative increase in the future period. The improvement of low flow state in the future period is related to the changes occurred in the state of climate scenarios. As the results indicated, most often, there is an increase in rainfall in dry seasons. Also, in different months of the wet season wet season, if the result of changes in quantity of rainfall is incremental, it can lead to an increase in river flow through groundwater recharge. On the other hand due to the limestone and karst forms in most of the basin area, water storage ability and increase the amount of river flow during low water season in this area is expected. The study on rainfall quantity in Tang Panj Sezar sub catchment also indicated that, there will be no significant increase or decrease in the quantity of rainfall in the dry season. Thus, it is expected that there will not be significant changes in low flow indices. In this sub catchment, changes in various low flow indices do not match perfectly, so more difficult to obtain reliable results. With regard to incremental changes of Q95, low flow index with less uncertainty, as well as improving indices of low flow in other sub-basins, it is possible to predict a relatively better state for low flow indices of Tang Panj Sezar in the future period.
Using temperature and rainfall scenarios to simulate river flow in the future, a relative increase of all three low flow indices Q70, Q90 and Q95 was predicted compared with the past period. Although all three of mentioned indices show the amount of low flow in the dry season, it is recommended that only two indices of Q90 and Q95 to assess the effects of climate change be considered. Q90 and Q95 indices are more suitable indices than Q70 for studying the effects of climate change on low flow state. These two indices indicate less quantity of flow in dry seasons; therefore, the changes of the two indices are more important in identifying the low flow state. However, there is less uncertainty in the estimation of the two Q90 and Q95 indices than Q70.

Flood is an important hydrological event which has considerable outcomes in human’s life. The frequency analysis of prior period is one of the ways for assessing this event. However, the selection method of extreme values can have a significant impact on frequency analysis of such events. Several approaches have been proposed for this purpose, including the annual maximum series (AMS) and the peaks over threshold (POT). In the AMS method, only the greatest event occurred in each year will be selected. But in POT method, regardless of the time of occurrence of extreme events, a threshold is determined and the values over threshold are participated in the frequency analysis. The question is that, how to determine the appropriate threshold. For this aim, some constraints have been made, satisfying them, optimal point can be determined. Golestan province is one of the major poles of agriculture in Iran. Thus, Arazkuseh station in this province, with longtime daily recorded discharge data during 1344-45 to 1388-89, was selected as the study area. Drawing time-series diagrams for discharge data, individual events were selected to be used in the POT method.Comparing the results of the extreme values frequency analysis indicated that the POT method provides higher quantiles than the AMS, in different return periods. Also, by plotting confidence intervals for different return periods, POT method presents less uncertainty. The optimal threshold of 47 cubic meters per second was obtained for flood frequency analysis.

Drought is a creeping natural phenomenon, which can occur in any region. Such phenomenon not only affects the region subjected to drought, but its adverse effects can also be extended to other adjacent regions. This phenomenon mainly starts with water deficiency (say less than long- term mean of variable under study such as rainfall, streamflow, groundwater level or soil moisture) and progress in time. This period can be ended by increasing the rainfall and reaching the mean level. Even after the ending of a drought period, its adverse effects can be continued for several months. Although, it is not possible (at least at this time) to prevent the occurrence of drought in a given region, it is not impossible to alleviate the drought consequences by scientific water management. Such a management should be employed before drought initiation as well as during it and continue on even after the end of the drought period. The frequency of the main drought characteristics is a major concern of this study. The Northwest of Iran recently encountered severe and prolonged droughts, such that a major portion of the Urmia Lake surface disappeared during the last drought in recent years. In order to study drought characteristics, we used the Reconnaissance Drought Index (RDI). This index is based on annual rainfall and potential reference crop evapotranspiration (abbreviated by PET here). This study employed the Monte Carlo simulation technique for synthetic data generation for analysis. The information from the 17 synoptic weather stations located in the North-west of Iran was used for drought analysis. Data was gathered from the Islamic Republic of Iran’s Meteorological Organization (IRIMO). In the first stage of research, the ratio of long term mean annual precipitation to evapotranspiration was calculated for each of the stations. For this purpose, the Penman-Montheis (FAO 56) method was selected for PET estimation. In the second stage, the 64 candidate statistical distributions were fitted for the mentioned RDI’s of each station. The best statistical distribution was selected among the 64 candidate distributions. The best fitted distribution was identified by the chi-square criterion. The parameters of the distribution were estimated by the Maximum Likelihood Estimation (MLE) scheme. Then 500 synthetic time series (each of them have the same number of observed data) were generated employing the parent population parameters. The three main drought characteristics (namely duration, severity and magnitude) were obtained for each of the mentioned artificial time series. The maximum values for each of the mentioned drought characteristic were selected for each year. Then, a new time series having the 500 elements were obtained by collecting the chosen values for each station. Once again the best distribution was selected for each series. Drought characteristics for different return periods (2, 10, 25, 50, 100 and 200 years) were estimated for each station. Preliminary results indicated that a negative trend existed in annual rainfall time series for almost all of the stations. On the other hand, the pattern of monthly PET histograms were more or less similar for all of the selected stations. The peak of the PET was mainly observed in the hottest month of year, whereas the lowest value of the monthly PET belonged to the coldest month of year. The results showed that the amount of annual rainfall time series decreases sharply, after the year 1991. However, PET values significantly increase for all of the selected stations. After calculation of RDI values, the histogram of annual RDI’s was plotted against the year. This is repeated for all of the selected stations. Figure. 6 shows the mentioned diagram for Tabriz station as an example. In the mentioned Figure, negative values of RDI (shown by red bars) indicated the drought years. A critical prolonged drought with a sixteen years duration period (neglecting the 2001 in which RDI value was a small positive value) was experienced in Tabriz. The maximum drought severity in Tabriz was estimated to be about -7 in RDI units. Urmia station experienced the longest drought period, starting from 1995 and ending in 2005. It can be concluded that although few sparse wet years were observed in some of the selected stations in the studied period, they cannot compensate the water deficiency accumulated during several consecutive years. The results showed that the lowest value of the ratio of drought severity in a 100 year return period to the corresponding value for 2 year return period was about 2.13 (belonged to the Tabriz station), whereas the highest value was 3.17 (belonged to the Tekab station). On the other hand, the lowest value for the ratio of drought duration in 100 year return period to its corresponding value for 2 year return period was 1.95 (experienced in the Makoo station). The highest mentioned ratio was 9.18 (observed in the Sardasht station). The lowest and highest value of the ratio of drought magnitude in 100 year return period to its corresponding value for 2 year return period were 1.17 and 2.74, respectively. The mentioned drought magnitude ratios were observed in the Urmia and the Khalkhal stations, respectively. The isoplethes of the three main drought characteristics (severity, magnitude, duration) for a 10 year return period was illustrated for the study area (Northwest of Iran). In the present study RDI values were used to analyze drought characteristics of Northwest of Iran. The Penman-Montheis method was used to estimate PET (needed for RDI) values of the stations. The main three drought characteristics were calculated for each of the 500 synthetic time series. The results showed that nearly all of the areas under study experienced severe and prolonged droughts. It can be concluded that a sharp decrease in annual precipitation as well as the increase in PET (due to greenhouse effects of consuming fossil fuels as the main source of energy in the region) from 1995 to 2005 was observed in the study area. Scientific management of available water in the study area is extremely vital to alleviate the adverse consequences of drought. Several economic and social problems were anticipated in these arid and semi-arid regions of Iran.

The common methods in the flood frequency analysis are used to examine the flood peak variable while each flood event consists of three naturally random variables: flood’s peak, volume and duration. Furthermore, it was essential to assume the variable of interest follows a specific parametric distribution chosen from an appropriate distributions’ family. With the aim to overcome these limitations a wide range of the well-known parametric distributions functions and non-parametric methods based on kernel density estimation and orthonormal series approximation is considered to estimate the distribution of flood variables. The investigated variables is extracted from annual maximum flood series at Ahvaz hydrometric station. In order to compare between the fitted parametric and non-parametric distributions to the data, we used the statistical criterions such as Akaike Information Criteria, Bayesian Information Criteria and the Root mean square error. We then used the chi-square goodness of fit test to examine that the acceptability of the chosen distribution in the previous stage. The results showed that the flood peak follows the parametric log Pearson type III distribution function while the non-parametric orthogonal series approximation were the best fit for the flood volume and flood duration. The estimated distributions based on the orthogonal series expansion were able to capture the graphical features of the data and the corresponding fitted densities reproduced the same unimodality or multi-modality that the original histogram of data illustrates. This is not the case when one uses the traditional methods to analyze the frequency of flood.

One of the most important parameters in design of hydraulic structures is the instantaneous peak discharge. In this research, 20 empirical formulas that have been developed all over the world to estimate the peak discharge are evaluated in 26 hydrologic stations of western catchments of Iran. Instantaneous peak discharge data during a 42 year period at these stations were selected. Frequency analysis was performed on these instantaneous peak discharges by eight probability distribution functions, and after the best fit test, the instantaneous peak discharges with return periods of 2, 5, 10, 25, 50, 100, 500 and 1000 year were estimated by Log Pierson type III distribution. Homogeneity test of catchments under study was also done by Dalrymple method. Linear and non-linear regression analyses were done and the empirical formulas were calibrated for the region, and then the best formula was recommended. For the recommended models, the mean error for 20 to 1000-year return periods was between 21.67% and 34.67%. Among the most important results of the study is the inaccuracy of Fuler formula in estimating the instantaneous peak discharge in western catchments of Iran.

In this study, in order to determe low flow conditions in Karkhe watershed, 5 indices of Q7,10, Q7,20, Q30,10, Q4,3 and Q95 were used for analyzing 12 hydrometric station data in the years of 1345-46 to 1380-81. Discharge data homogeneity was performed by Run Test. The Q95 index was determined by flow duration curve (FDC) and other indices were determined using 4, 7 and 30-day low flow frequency analysis. After calculating the indices, periods of low flows were determined. The indices were regionalized by Kriging method. The results showed that for the most stations, low stream flows happened in the years of 1345-46, 1377-78, 1378-79, 1379-80 and 1380-81 and the percentages of stations having low flows in these years were 68, 92, 84, 75 and 59, respectively. According to the regional maps of low flows in Karkhe watershed, maximum low flows are located in central and southern areas and all of the mentioned indices decrease from south to the north of this watershed.

Low flow estimation and its characteristics play an important role in hydrologic studies. However, some low flow events are ignored compared with the lowest annual low flow that may have high risk. These events are taken into consideration by the use of partial duration or peak over threshold models. In this study, a 7-day low flow was applied for frequency distribution and threshold, and the lower events were considered as the number of low flow event ( ) to study seasonal variation of low flows together with two graphical methods. The results showed two major low flow seasons, and for other times of the year, the low flow events are negligible. At last, the region was divided into homogeneous groups based on seasonal variation of low flows.